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Run deep-learning single-cell analyses for integration, multimodal modeling, and mapping.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "scvi-tools" skill from askskill: 1. Download https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/bio-research/skills/scvi-tools/SKILL.md 2. Save it as ~/.claude/skills/scvi-tools/SKILL.md 3. Reload skills and tell me it's ready
I have two single-cell RNA-seq datasets from different batches. Use scVI or scANVI for data integration and batch correction, and explain how to build the latent space, assess integration quality, and produce visualization outputs.
A scVI/scANVI workflow with key parameter guidance, integration evaluation methods, and visualization deliverables.
Please use totalVI to analyze my CITE-seq data, jointly model RNA and protein expression, perform denoising and clustering, interpret cell types, and explain which features are most discriminative.
Returns a totalVI analysis workflow, joint embeddings, clustering and cell-type interpretation, plus a summary of key RNA/protein features.
I want to map new single-cell samples onto an existing reference atlas. Use scANVI or scArches to design a label transfer and reference mapping workflow, and explain how to handle unknown cell types and batch differences.
Provides a reference mapping plan, label transfer steps, recommendations for unknown classes, and methods to control batch effects.
This skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics.
scripts/ to avoid rewriting common codereferences/environment_setup.mdreferences/troubleshooting.md| Data Type | Model | Primary Use Case |
|---|---|---|
| scRNA-seq | scVI | Unsupervised integration, DE, imputation |
| scRNA-seq + labels | scANVI | Label transfer, semi-supervised integration |
| CITE-seq (RNA+protein) | totalVI | Multi-modal integration, protein denoising |
| scATAC-seq | PeakVI | Chromatin accessibility analysis |
| Multiome (RNA+ATAC) | MultiVI | Joint modality analysis |
| Spatial + scRNA reference | DestVI | Cell type deconvolution |
| RNA velocity | veloVI | Transcriptional dynamics |
| Cross-technology | sysVI | System-level batch correction |
| Workflow | Reference File | Description |
|---|---|---|
| Environment Setup | references/environment_setup.md | Installation, GPU, version info |
| Data Preparation | references/data_preparation.md | Formatting data for any model |
| scRNA Integration | references/scrna_integration.md | scVI/scANVI batch correction |
| ATAC-seq Analysis | references/atac_peakvi.md | PeakVI for accessibility |
| CITE-seq Analysis | references/citeseq_totalvi.md | totalVI for protein+RNA |
| Multiome Analysis | references/multiome_multivi.md | MultiVI for RNA+ATAC |
| Spatial Deconvolution | references/spatial_deconvolution.md | DestVI spatial analysis |
| Label Transfer | references/label_transfer.md | scANVI reference mapping |
| scArches Mapping | references/scarches_mapping.md | Query-to-reference mapping |
| Batch Correction | references/batch_correction_sysvi.md | Advanced batch methods |
| RNA Velocity | references/rna_velocity_velovi.md | veloVI dynamics |
| Troubleshooting | references/troubleshooting.md | Common issues and solutions |
Modular scripts for common workflows. Chain together or modify as needed.
| Script | Purpose | Usage |
|---|---|---|
prepare_data.py | QC, filter, HVG selection | python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch |
train_model.py | Train any scvi-tools model | python scripts/train_model.py prepared.h5ad results/ --model scvi |
cluster_embed.py | Neighbors, UMAP, Leiden | python scripts/cluster_embed.py adata.h5ad results/ |
differential_expression.py | DE analysis | python scripts/differential_expression.py model/ adata.h5ad de.csv --groupby leiden |
transfer_labels.py | Label transfer with scANVI | python scripts/transfer_labels.py ref_model/ query.h5ad results/ |
integrate_datasets.py | Multi-dataset integration | python scripts/integrate_datasets.py results/ data1.h5ad data2.h5ad |
validate_adata.py | Check data compatibility | python scripts/validate_adata.py data.h5ad --batch-key batch |
# 1. Validate input data
python scripts/validate_adata.py raw.h5ad --batch-key batch --suggest
# 2. Prepare data (QC, HVG selection)
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